{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T07:50:21Z","timestamp":1790063421463,"version":"4.0.1"},"reference-count":25,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T00:00:00Z","timestamp":1579824000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"name":"OHSRC part of Oxford Hospitals Charity"},{"name":"Thames Valley & South Midlands Clinical Research Network"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acta Ophthalmologica"],"published-print":{"date-parts":[[2020,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Recent advances in deep learning have seen an increase in its application to automated image analysis in ophthalmology for conditions with a high prevalence. We wanted to identify whether deep learning could be used for the automated classification of optical coherence tomography (OCT) images from patients with Stargardt disease (STGD) using a smaller dataset than traditionally used.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Sixty participants with STGD and 33 participants with a normal retinal OCT were selected, and a single OCT scan containing the centre of the fovea was selected as the input data. Two approaches were used: Model 1 \u2013 a pretrained convolutional neural network (CNN); Model 2 \u2013 a new CNN architecture. Both models were evaluated on their accuracy, sensitivity, specificity and Jaccard similarity score (JSS).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>About 102 OCT scans from participants with a normal retinal OCT and 647 OCT scans from participants with STGD were selected. The highest results were achieved when both models were implemented as a binary classifier: Model 1 \u2013 accuracy 99.6%, sensitivity 99.8%, specificity 98.0% and JSS 0.990; Model 2 \u2013 accuracy 97.9%, sensitivity 97.9%, specificity 98.0% and JSS 0.976.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The deep learning classification models used in this study were able to achieve high accuracy despite using a smaller dataset than traditionally used and are effective in differentiating between normal OCT scans and those from patients with STGD. This preliminary study provides promising results for the application of deep learning to classify OCT images from patients with inherited retinal diseases.<\/jats:p><\/jats:sec>","DOI":"10.1111\/aos.14353","type":"journal-article","created":{"date-parts":[[2020,1,25]],"date-time":"2020-01-25T04:39:30Z","timestamp":1579927170000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Automated classification of normal and Stargardt disease optical coherence tomography images using deep learning"],"prefix":"10.1111","volume":"98","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7496-6121","authenticated-orcid":false,"given":"Mital","family":"Shah","sequence":"first","affiliation":[{"name":"Oxford Eye Hospital Oxford University Hospitals NHS Foundation Trust Oxford UK"},{"name":"Nuffield Laboratory of Ophthalmology Nuffield Department of Clinical Neurosciences University of Oxford Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana","family":"Roomans Ledo","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Engineering Department of Engineering Science University of Oxford Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Rittscher","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Engineering Department of Engineering Science University of Oxford Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,1,24]]},"reference":[{"key":"e_1_2_5_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0040-6"},{"key":"e_1_2_5_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2018.10.007"},{"key":"e_1_2_5_4_1","unstructured":"Centers for Medicare and Medicaid Services(n.d.).Medicare provider utilization and payment data. 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